Wired to be connected? Links between mobile technology engagement, intertemporal preference and frontostriatal white matter connectivity.

Youth around the world are increasingly dependent on social media and mobile smartphones. This phenomenon has generated considerable speculation regarding the impacts of extensive technology engagement on cognitive development and how these habits might be 'rewiring' the brains of those growing up in a heavily digital era. In an initial study conducted with healthy young adults, we utilized behavioral and self-report measures to demonstrate associations between smartphone usage habits (assessed both subjectively and objectively) and individual differences in intertemporal preference and reward sensitivity. In a follow-up neuroimaging study, we used probabilistic tractography of diffusion-weighted images to determine how these individual difference characteristics might relate to variation in white matter connectivity, focusing on two dissociable pathways-one connecting the ventral striatum (vSTR) with the ventromedial prefrontal cortex (vmPFC) and the other connecting the vSTR with the dorsolateral prefrontal cortex (dlPFC). Regression analyses revealed opposing patterns of association, with stronger vSTR-vmPFC connectivity corresponding to increased mobile technology engagement but stronger vSTR-dlPFC connectivity corresponding to decreased engagement. Taken together, the results of these two studies provide important foundational evidence for both neural and cognitive factors that can be linked to how individuals engage with mobile technology.

Substance abuse and white matter: Findings, limitations, and future of diffusion tensor imaging research.

Individuals who abuse substances often differ from nonusers in their brain structure. Substance abuse and addiction is often associated with atrophy and pathology of grey matter, but much less is known about the role of white matter, which constitutes over half of human brain volume. Diffusion tensor imaging (DTI), a method for non-invasively estimating white matter, is increasingly being used to study addiction and substance abuse. Here we review recent DTI studies of major substances of abuse (alcohol, opiates, cocaine, cannabis, and nicotine substance abuse) to examine the relationship, specificity, causality, and permanence of substance-related differences in white matter microstructure. Across substance, users tended to exhibit differences in the microstructure of major fiber pathways, such as the corpus callosum. The direction of these differences, however, appeared substance-dependent. The subsample of longitudinal studies reviewed suggests that substance abuse may cause changes in white matter, though it is unclear to what extent such alterations are permanent. While collectively informative, some studies reviewed were limited by methodological and technical approach. We therefore also provide methodological guidance for future research using DTI to study substance abuse.

Good Things for Those Who Wait: Predictive Modeling Highlights Importance of Delay Discounting for Income Attainment.

Income is a primary determinant of social mobility, career progression, and personal happiness. It has been shown to vary with demographic variables like age and education, with more oblique variables such as height, and with behaviors such as delay discounting, i.e., the propensity to devalue future rewards. However, the relative contribution of each these salary-linked variables to income is not known. Further, much of past research has often been underpowered, drawn from populations of convenience, and produced findings that have not always been replicated. Here we tested a large (n = 2,564), heterogeneous sample, and employed a novel analytic approach: using three machine learning algorithms to model the relationship between income and age, gender, height, race, zip code, education, occupation, and discounting. We found that delay discounting is more predictive of income than age, ethnicity, or height. We then used a holdout data set to test the robustness of our findings. We discuss the benefits of our methodological approach, as well as possible explanations and implications for the prominent relationship between delay discounting and income.

Dissociable frontostriatal white matter connectivity underlies reward and motor impulsivity.

Dysfunction of cognitive control often leads to impulsive decision-making in clinical and healthy populations. Some research suggests that a generalized cognitive control mechanism underlies the ability to modulate various types of impulsive behavior, while other evidence suggests different forms of impulsivity are dissociable, and rely on distinct neural circuitry. Past research consistently implicates several brain regions, such as the striatum and portions of the prefrontal cortex, in impulsive behavior. However the ventral and dorsal striatum are distinct in regards to function and connectivity. Nascent evidence points to the importance of frontostriatal white matter connectivity in impulsivity, yet it remains unclear whether particular tracts relate to different control behaviors. Here we used probabilistic tractography of diffusion imaging data to relate ventral and dorsal frontostriatal connectivity to reward and motor impulsivity measures. We found a double dissociation such that individual differences in white matter connectivity between the ventral striatum and the ventromedial prefrontal cortex and dorsolateral prefrontal cortex was associated with reward impulsivity, as measured by delay discounting, whereas connectivity between dorsal striatum and supplementary motor area was associated with motor impulsivity, but not vice versa. Our findings suggest that (a) structural connectivity can is associated with a large amount of behavioral variation; (b) different types of impulsivity are driven by dissociable frontostriatal neural circuitry.

Neural connections foster social connections: a diffusion-weighted imaging study of social networks.

Although we know the transition from childhood to adulthood is marked by important social and neural development, little is known about how social network size might affect neurocognitive development or vice versa. Neuroimaging research has identified several brain regions, such as the amygdala, as key to this affiliative behavior. However, white matter connectivity among these regions, and its behavioral correlates, remain unclear. Here we tested two hypotheses: that an amygdalocentric structural white matter network governs social affiliative behavior and that this network changes during adolescence and young adulthood. We measured social network size behaviorally, and white matter microstructure using probabilistic diffusion tensor imaging in a sample of neurologically normal adolescents and young adults. Our results suggest amygdala white matter microstructure is key to understanding individual differences in social network size, with connectivity to other social brain regions such as the orbitofrontal cortex and anterior temporal lobe predicting much variation. In addition, participant age correlated with both network size and white matter variation in this network. These findings suggest the transition to adulthood may constitute a critical period for the optimization of structural brain networks underlying affiliative behavior.